#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

#-*- coding: utf-8 -*-

import parl
from parl import layers


class Model(parl.Model):
    def __init__(self, act_dim):
        # ch = 256
        # self.c1 = layers.conv2d(num_filters=ch, filter_size=(2, 2), act="relu", padding="SAME", bias_attr=True)
        # self.c2 = layers.conv2d(num_filters=ch, filter_size=(2, 2), act="relu", padding="SAME", bias_attr=True)
        # self.c3 = layers.conv2d(num_filters=ch, filter_size=(2, 2), act="relu", padding="SAME", bias_attr=True)
        # self.c4 = layers.conv2d(num_filters=ch, filter_size=(2, 2), act="relu", padding="SAME", bias_attr=True)
        # self.c5 = layers.conv2d(num_filters=ch, filter_size=(2, 2), act="relu", padding="SAME", bias_attr=True)
        # self.fc = layers.fc(size=act_dim, act='softmax')

        ch1 = 256
        ch2 = 4096
        self.c1 = layers.conv2d(num_filters=ch1, filter_size=(2, 1), act="relu", bias_attr=True)
        self.c2 = layers.conv2d(num_filters=ch1, filter_size=(1, 2), act="relu", bias_attr=True)
        self.c11 = layers.conv2d(num_filters=ch2, filter_size=(2, 1), act="relu", bias_attr=True)
        self.c12 = layers.conv2d(num_filters=ch2, filter_size=(1, 2), act="relu", bias_attr=True)
        self.c21 = layers.conv2d(num_filters=ch2, filter_size=(2, 1), act="relu", bias_attr=True)
        self.c22 = layers.conv2d(num_filters=ch2, filter_size=(1, 2), act="relu", bias_attr=True)
        self.fc = layers.fc(size=act_dim, act='softmax')



    def forward(self, obs):  # 可直接用 model = Model(5); model(obs)调用
        # out = self.fc(layers.flatten(self.c5(self.c4(self.c3(self.c2(self.c1(obs)))))))

        r1 = layers.flatten(self.c1(obs), axis=1)
        r2 = layers.flatten(self.c2(obs), axis=1)
        r11 = layers.flatten(self.c11(self.c1(obs)), axis=1)
        r12 = layers.flatten(self.c12(self.c1(obs)), axis=1)
        r21 = layers.flatten(self.c21(self.c2(obs)), axis=1)
        r22 = layers.flatten(self.c22(self.c2(obs)), axis=1)
        hidden = layers.concat(input=[r1, r2, r11, r12, r21, r22], axis=1)
        out = self.fc(hidden)

        return out
